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Mukhammad Fayzoda | VK

Spotify is marktleider, en toch schrapt het 17 procent van ...

Sverige tog emot tusentals ensamkommande barn 2015, särskilt pojkar. En majoritet av dem som fick uppehållstillstånd är i dag kvar i landet och etablerade på arbetsmarknaden, inte minst inom vården. – Det är betydligt vanligare i de här grupperna än bland personer födda i Sverige ...

Majoritet av ensamkommande unga har jobb

Delhi Lieutenant Governor VK Saxena has granted permission to conduct an enquiry under Section 17 A of the Prevention Corruption Act (PCA) 1988, against a sub-registrar of the Revenue Department, Government of National Capital Territory of Delhi (GNCTD ...

Delhi LG nod to probe against revenue officer for bribery

At this point in time, we do not do segment wise reporting, but all I can say very clearly is that the segments that we operate in have significant high barriers to entry and are insulated from many margin pressures and we enjoy healthy margin profile across the three verticals.

Blue Jet management on which segments will drive growth for the company

Bhubaneswar: As many as 27 service associations of the state government and several private organisations Tuesday met 5T and ‘Nabin Odisha’ chairman VK Pandian and congratulated him for the new assignment. An official release by the CMO said the ...

27 service associations meet 5T chairman VK Pandian, greet him

As many as 27 service associations of the state government and several private organisations Tuesday met 5T and ‘Nabin Odisha'

27 service associations meet 5T chairman VK Pandian

Vinod Kumar Sibal, former Director General of Hydrocarbons the regulatory body under the petroleum ministry -- has been chargesheeted by the Central Bureau of Investigation in an alleged corruption case.

CBI Charges Ex-Chief Of Hydrocarbons In 2005 Corruption Case

They allegedly siphoned government funds to the tune of Rs 2.44 crore NEW DELHI: Lieutenant Governor of Delhi VK Saxena has accorded sanction for the prosecution of 10 Delhi Police officials in a case of embezzlement of government funds to the tune of Rs 2.

Delhi Lieutenant Governor VK Saxena nod to prosecution of 10 cops for embezzlement

Elakkiyadasan finished with a timing of 10.36 sec ahead of Pranav Gurav of Maharashtra who finished in 10.41 sec and SSCB's Saurabh Rajesh who won the bronze with a timing of 10.45 sec.

37th National Games: VK Elakkiyadasan, SS Sneha crowned fastest athletes

VK.com

I conducted a lot of interesting and effective experiments. Some of them increased our metrics significantly, some of them less. I left the company several days ago, but I want to share something interesting. Today I will tell you how I managed to increase the user engagement of VK Messenger by adding a block of recommendations of contacts. And what is the most important – I will honestly tell you about the problems I faced and how I solved them because I think that is the most interesting part of stories like this one. The block of recommendations for contacts is located at the top of the chat list in VK Messenger. Here are the steps I took to integrate this feature: First of all, I studied the existing messengers and saw similar features in the products of our competitors like Facebook and Instagram, but I wasn't sure if it would fit well with VK Messenger. I had a hypothesis that it could increase the engagement of our users, so I decided to give it a try. In the beginning, I launched this feature based on a heuristical algorithm: people whom you added as friends recently, phone contacts, those with whom you chatted earlier, etc. I would like to conduct a RAT (riskiest assumptions test) in the cheapest possible way to make sure I don't spend a lot of resources on things I am not sure would give us meaningful results. While I was sure that this MVP was really viable, I invited the machine learning team to help us improve the recommendations. Eventually, when I launched this feature based on an ML algorithm on the Web version, I got a +1% increase in time spent and +0.72% in the number of sent messages. I launched it on our Android and iOS mobile applications. Now, here are the problems I faced in developing and launching this experiment: Our team uses the messenger for day-to-day work, and they were concerned if this block with recommended contacts would distract the users from normal communication in existing chats. I collected feedback from our colleagues and did user interviews to improve the user experience. As a result, I decreased the size of the block a little bit and added a toggle to turn off these recommendations for those who don't like them. Also, it's possible to hide certain people from the list if you like the recommendations but don't want to see some of them. Later, when I launched an experiment on our real users I recognized that this opinion was biased because our core audience consisted of young scholars and students, and they found it entertaining to see new people to chat with. I have different types of users, so the switching off feature wasn't distracting for our core users, but also it was well-received by those who wanted to see just their chat list without recommendations. When I launched this experiment on the Android version I saw a slight initial decrease in time spent on the Stories feature, but the total time spent on the platform increased. This happened because the number of users who spent their time chatting with recommended contacts grew significantly. So I decided that this feature should be launched for 100% of our users. Later, I would improve the metrics of Stories by adding the Stories feature to the Messenger Tab, making it a win-win for both of us. When I launched this experiment on iOS, our support team came to us with complaints that the users don't understand what it is and what type of people they see in this block. In just one night our developer created a chatbot that explained the algorithm and asked for feedback so that I could make it even better. I launched it the next day, placing it in the first position of the block with recommended contacts. To sum it up, let me point out the most important parts of the development process: It's important to launch the feature within your team first – eat your own dog food! Collect the problems and ideas on how to improve them, then try to solve these problems. Be careful about biased opinions, though. Conduct as many user interviews as possible to ensure you don't miss any important moments. Launch only on the part of the users first, get feedback, and fix the problems fast. Then launch it step by step, gradually increasing the percentage of the users who see this feature. Always try to find the most effective solutions that need less effort. It will help you move forward faster. Check that you don't screw up other parts of the whole service. And if you see that something is going wrong, find the correct way to calculate profits and losses to make the right decision. Be brave! Changing the parts of your service that weren't changed for years may seem risky at times, but being bold and fast is the only way to create a big impact.

How to Add Contact Recommendations to VK Messenger

Authors:
(1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk;
(2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Table of Links Abstract and 1 Introduction 2 Related Work 3 Framework 4 Main Results 5 A Case Study on Shortest-Path Distance 6 Conclusion and Discussion, and References 7 Proof of Theorem 1 8 Proof of Theorem 2 9 Procedure for Solving Eq. (6) 10 Additional Experiments Details and Results 11 Other Potential Applications 3 Framework In this section, we introduce our proposed framework, which aims to investigate the intricate relationship between topology awareness and generalization performance in GNNs. The main idea of the framework is to use metric distortion to characterize the topology awareness of GNNs. With this characterization, we are able to show the relation between topology awareness of GNNs and their generalization performance. 3.1 GNN Preliminaries Let G = (V, E, X) be an undirected graph where V = {1, 2, ..., n} is the set of n nodes and E ⊆ V × V is the set of edges. Let X = {x1, x2, ..., xn} denote the set of node attribute vectors corresponding to all the nodes in V. More specifically, xv ∈ X is an m-dimensional vector which captures the attribute values of node v ∈ V. In addition, each vertex v ∈ V is associated with a node label yv ∈ {1, ..., c}, where c is the total number of classes. For our analysis, we focus on the transductive node classification setting [47], where the graph G are observed prior to learning and labels are only available for a small subset of nodes V0 ⊂ V. Without loss of generality, we treat G as fixed throughout our analysis and the randomness comes from the labels y. Given a small set of the labelled nodes, V0 ⊂ V, the task of node-level semi-supervised learning is to learn a classifier f, such that yˆv = f(v), from a hypothesis space F and perform it on the remaining unlabeled nodes. Let L(., .) 7→ R+ be a bounded continuous loss function. We denote by RL(f, V) the generalization risk of the classifier f with respect to L and V and it is defined as follows, In our case, the hypothesis space is captured by a GNN and an additional read-out function. GNNs are commonly used as an embedding function and adopt a neighbourhood aggregation scheme, where the representation of a node is learnt iteratively by aggregating representations of its neighbours. After k iterations of aggregation, a node’s representation captures the information within its k-hop neighbourhood in the graph. Formally, the k-th layer of a GNN can be written as follows, It should be noted that the distance function in Def. 2 is not symmetrical, and Eq. 4 captures the maximal structural difference of vertices in V1 to V2. To provide further insight into the concept of a structural subgroup, let’s consider a scenario where V0 ⊂ V represents the training set and ds(.) represents the shortest-path distance. In this context, we can partition the remaining vertices V\V0 into different structural subgroups based on their shortest-path distance to the nearest vertex in the training set. Specifically, we can define Vk = {v ∈ V\V0|Ds(v, V0) = k}. However, it is important to emphasize that our analysis does not assume a specific partition scheme, and the framework remains adaptable to various scenarios. 3.3 Topology Awareness To characterize and formalize the topology awareness of GNNs, we introduce the concept of distortion, which measures the degree to which the graph structure is preserved in the embedding space. Mathematically, the distortion of a function between two general metric spaces is defined as, The definition above captures the maximum expansion and minimum contraction between the two metric spaces. In the extreme case when α = 1, the mapping γ is the so-called isometric mapping that perfectly preserves the distance between any two points in the space. In other words, for any two points x and y in the domain of the function, their distance d(x, y) in the original metric space is equal to the distance d ′ (γ(x), γ(y)) in the target metric space (up to a constant scaling factor). If the distortion rate of the mapping is close to 1, then this mapping is said to have a low distortion rate. In the context of GNNs, evaluating distortion involves a straightforward comparison of embeddings in the embedding domain with the structural measures on the graph, as demonstrated in the case study. If the distortion rate between the graph domain and the embedding domain is close to 1, the resulting embedding would largely preserve the corresponding structural information from the graph space, thereby indicating high topology awareness. Consequently, distortion can be employed to measure the topology awareness of GNNs concerning the corresponding structural properties (e.g., shortest-path distance) measured in the graph domain. Consequently, distortion can be employed to measure the topology awareness of GNNs concerning the corresponding structural properties (e.g., shortest-path distance) measured in the graph domain. This concept of distortion is agnostic to specific structural configurations, enabling the study of diverse graph structures using appropriate distance functions. Moreover, its estimation can be readily achieved by comparing the embedding with the original structural measure, as illustrated later in the case study This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk; (2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Authors: Authors: (1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk; (2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Framework 3 Framework 4 Main Results 4 Main Results 5 A Case Study on Shortest-Path Distance 5 A Case Study on Shortest-Path Distance 6 Conclusion and Discussion, and References 6 Conclusion and Discussion, and References 7 Proof of Theorem 1 7 Proof of Theorem 1 8 Proof of Theorem 2 8 Proof of Theorem 2 9 Procedure for Solving Eq. (6) 9 Procedure for Solving Eq. (6) 10 Additional Experiments Details and Results 10 Additional Experiments Details and Results 11 Other Potential Applications 11 Other Potential Applications 3 Framework In this section, we introduce our proposed framework, which aims to investigate the intricate relationship between topology awareness and generalization performance in GNNs. The main idea of the framework is to use metric distortion to characterize the topology awareness of GNNs. With this characterization, we are able to show the relation between topology awareness of GNNs and their generalization performance. 3.1 GNN Preliminaries Let G = (V, E, X) be an undirected graph where V = {1, 2, ..., n} is the set of n nodes and E ⊆ V × V is the set of edges. Let X = {x1, x2, ..., xn} denote the set of node attribute vectors corresponding to all the nodes in V. More specifically, xv ∈ X is an m-dimensional vector which captures the attribute values of node v ∈ V. In addition, each vertex v ∈ V is associated with a node label yv ∈ {1, ..., c}, where c is the total number of classes. For our analysis, we focus on the transductive node classification setting [47], where the graph G are observed prior to learning and labels are only available for a small subset of nodes V0 ⊂ V. Without loss of generality, we treat G as fixed throughout our analysis and the randomness comes from the labels y. Given a small set of the labelled nodes, V0 ⊂ V, the task of node-level semi-supervised learning is to learn a classifier f, such that yˆv = f(v), from a hypothesis space F and perform it on the remaining unlabeled nodes. Let L(., .) 7→ R+ be a bounded continuous loss function. We denote by RL(f, V) the generalization risk of the classifier f with respect to L and V and it is defined as follows, In our case, the hypothesis space is captured by a GNN and an additional read-out function. GNNs are commonly used as an embedding function and adopt a neighbourhood aggregation scheme, where the representation of a node is learnt iteratively by aggregating representations of its neighbours. After k iterations of aggregation, a node’s representation captures the information within its k-hop neighbourhood in the graph. Formally, the k-th layer of a GNN can be written as follows, It should be noted that the distance function in Def. 2 is not symmetrical, and Eq. 4 captures the maximal structural difference of vertices in V1 to V2. To provide further insight into the concept of a structural subgroup, let’s consider a scenario where V0 ⊂ V represents the training set and ds(.) represents the shortest-path distance. In this context, we can partition the remaining vertices V\V0 into different structural subgroups based on their shortest-path distance to the nearest vertex in the training set. Specifically, we can define Vk = {v ∈ V\V0|Ds(v, V0) = k}. However, it is important to emphasize that our analysis does not assume a specific partition scheme, and the framework remains adaptable to various scenarios. 3.3 Topology Awareness To characterize and formalize the topology awareness of GNNs, we introduce the concept of distortion, which measures the degree to which the graph structure is preserved in the embedding space. Mathematically, the distortion of a function between two general metric spaces is defined as, The definition above captures the maximum expansion and minimum contraction between the two metric spaces. In the extreme case when α = 1, the mapping γ is the so-called isometric mapping that perfectly preserves the distance between any two points in the space. In other words, for any two points x and y in the domain of the function, their distance d(x, y) in the original metric space is equal to the distance d ′ (γ(x), γ(y)) in the target metric space (up to a constant scaling factor). If the distortion rate of the mapping is close to 1, then this mapping is said to have a low distortion rate. In the context of GNNs, evaluating distortion involves a straightforward comparison of embeddings in the embedding domain with the structural measures on the graph, as demonstrated in the case study. If the distortion rate between the graph domain and the embedding domain is close to 1, the resulting embedding would largely preserve the corresponding structural information from the graph space, thereby indicating high topology awareness. Consequently, distortion can be employed to measure the topology awareness of GNNs concerning the corresponding structural properties (e.g., shortest-path distance) measured in the graph domain. Consequently, distortion can be employed to measure the topology awareness of GNNs concerning the corresponding structural properties (e.g., shortest-path distance) measured in the graph domain. This concept of distortion is agnostic to specific structural configurations, enabling the study of diverse graph structures using appropriate distance functions. Moreover, its estimation can be readily achieved by comparing the embedding with the original structural measure, as illustrated later in the case study This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Framework for Analyzing Topology Awareness and Generalization in Graph Neural Networks

#blockchain-API Writing Contest Deadline Extended: Check Out dRPC’s Tips for Writing a Strong Entry 

#blockchain-API Skryfwedstryd Sperdatum verleng: Kyk na dRPC se wenke om 'n sterk inskrywing te skryf 

#blockchain-API Qillqañ atipt’asiw pachax jilxatayatawa: dRPC ukan iwxt’awinakap uñakipt’añäni, mä ch’aman qillqt’äwi qillqt’añataki 

Крайният срок за конкурс за писане на #blockchain-API е удължен: Вижте съветите на dRPC за писане на силен запис 

Se extiende la fecha límite para el concurso de redacción de API de blockchain: consulte los consejos de dRPC para escribir una propuesta sólida

Batas Waktu Kontes Penulisan #blockchain-API Diperpanjang: Simak Tips dRPC untuk Menulis Entri yang Kuat

#blockchain-API ライティング コンテストの締め切りが延長されました: 強力なエントリーを書くための dRPC のヒントをご覧ください

#blockchain-API rakstīšanas konkursa termiņš ir pagarināts: skatiet dRPC padomus, lai uzrakstītu spēcīgu pieteikumu 

#blockchain-API Letšatši la Mafelelo la Phadišano ya go Ngwala le Atološitšwe: Lekola dRPC’s Tips for Writing a Strong Entry 

#blockchain-API Qillqana atipanakuy p’unchaw mast’arisqa: Qhaway dRPC’s Yuyaychaykunata Sinchi Yaykuy qillqanapaq 

Срок подачи заявок на конкурс по написанию #blockchain-API продлен: ознакомьтесь с советами dRPC по написанию сильной заявки

#blockchain-API எழுதும் போட்டிக்கான காலக்கெடு நீட்டிக்கப்பட்டது: வலுவான நுழைவு எழுதுவதற்கான dRPC இன் உதவிக்குறிப்புகளைப் பாருங்கள் 

#blockchain-API ውድድር ጽሑፍ ናይ መወዳእታ ግዜ ተናዊሑ: dRPC’s ምኽርታት ንጽሕፈት ሓያል ምእታው ርአ 

Controlling Automated Vehicles on Large Lane-free Roundabouts (Extended Version): References

With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google, GE Healthcare, Siemens Healthineers, and many other leading AI startups, these AI dreams are achieving more feasibility. However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements. With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google , GE Healthcare , Siemens Healthineers , and many other leading AI startups, these AI dreams are achieving more feasibility. 46 percent statistical concern Google GE Healthcare Siemens Healthineers However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. GE Healthcare Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. Siemens Healthineers According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” VKTR A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. host of other startups Can we trust AI with our healthcare data? Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. Definitive identified invasive This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. paper A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” GDPR and CCPA There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements.

As Healthcare AI Advances, How Do we Balance the Benefits With Privacy Concerns?

The First Amendment Implications of TikTok's Ban

A Comparative Algorithm Audit of Conspiracies on the Net: Conclusion and Bibliography

Welcome to our brand new series - The HackerNoon Company of the Week, where we feature the top tech brands from our tech company database, making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike.This week, we present our business blogging partner__inDrive__ (former inDriver, short for “Independent Drivers“) - a ride-hailing service with one big edge from its competitors: the fare for the trip is set by the passengers themselves! Who doesn't like autonomy and self sovereignty ✊ Wanna be part of HackerNoon’s evergreen tech company database? Request Your Tech Company Page on HackerNoon! Meet inDrive: #FunFact Did you know that inDrive started as a small community of independent drivers in Yakutsk? According to the inDrive's ex-CTO: "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive.In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." From a small community of independent drivers to one of the top ride hailing services known globally, inDrive has come a long way! inDrive’s Featured Business Stories on HackerNoon inDrive.tech has shared some amazing content via the HackerNoon Business Blogging Program, and has generated 3 months 9 days 6 hours and 36 minutes of reading time! Here are our top 3 favorite stories by InDrive: Understanding Dynamic Programming So You Can Use It Effectively
How We Automated the Verification of Car Photos
How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook That's all this week, hackers!Stay Creative, Stay Iconic.The__HackerNoon Team__ Welcome to our brand new series - The HackerNoon Company of the Week, where we feature the top tech brands from our tech company database , making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. This week, we present our business blogging partner__ inDrive __ (former inDriver, short for “Independent Drivers“) - a ride-hailing service with one big edge from its competitors: the fare for the trip is set by the passengers themselves! Who doesn't like autonomy and self sovereignty ✊ tech company database tech company database inDrive Wanna be part of HackerNoon’s evergreen tech company database? Request Your Tech Company Page on HackerNoon! Wanna be part of HackerNoon’s evergreen tech company database? Request Your Tech Company Page on HackerNoon ! Request Your Tech Company Page on HackerNoon Meet inDrive: #FunFact Did you know that inDrive started as a small community of independent drivers in Yakutsk? According to the inDrive's ex-CTO : inDrive's ex-CTO inDrive's ex-CTO "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive.In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive.In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." "As far as cab services were concerned, it was difficult and expensive to get a ride in Yakutsk before inDrive made its appearance on the scene. Things worked the old-fashioned way, and you had to call first and book a vehicle via the dispatcher and then wait a long time for the driver to arrive. In the winter of 2012, several smart guys created a Vkontakte group called Community of Independent Drivers of Yakutsk. In it, you could indicate that you needed to go from point A to point B and leave your phone number. Then a caring driver who just happened to be headed that way might call you to arrange a ride." From a small community of independent drivers to one of the top ride hailing services known globally, inDrive has come a long way! inDrive’s Featured Business Stories on HackerNoon inDrive.tech has shared some amazing content via the HackerNoon Business Blogging Program , and has generated 3 months 9 days 6 hours and 36 minutes of reading time! Here are our top 3 favorite stories by InDrive: inDrive.tech HackerNoon Business Blogging Program Understanding Dynamic Programming So You Can Use It Effectively How We Automated the Verification of Car Photos How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook Understanding Dynamic Programming So You Can Use It Effectively Understanding Dynamic Programming So You Can Use It Effectively How We Automated the Verification of Car Photos How We Automated the Verification of Car Photos How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook How to Run 5000+ Tests on Mobile Devices Everyday; From inDrive's Playbook That's all this week, hackers! Stay Creative, Stay Iconic. The__ HackerNoon Team __ HackerNoon Team

Meet inDrive: The HackerNoon Company of the Week

মিট ইনড্রাইভ: সপ্তাহের হ্যাকারনুন কোম্পানি

Lernen Sie inDrive kennen: Das HackerNoon-Unternehmen der Woche

Conozca inDrive: la empresa de la semana de HackerNoon

Rencontrez inDrive : l'entreprise HackerNoon de la semaine

इनड्राइव से मिलिए: इस सप्ताह की हैकरनून कंपनी

inDrive のご紹介: 今週の HackerNoon 企業

inDrive에서 만나보세요: 금주의 HackerNoon 회사

Conheça inDrive: a empresa HackerNoon da semana

Встречайте inDrive: компания недели HackerNoon

inDrive'da Tanışın: Haftanın HackerNoon Şirketi

Gặp gỡ inDrive: Công ty HackerNoon của tuần

结识 inDrive：本周 HackerNoon 公司

Memes and Narrative Strategies in the Russo-Ukrainian War: A Comprehensive Study

রুশো-ইউক্রেনীয় যুদ্ধে মেমস এবং বর্ণনামূলক কৌশল: একটি ব্যাপক অধ্যয়ন

Meme und Erzählstrategien im Russisch-Ukrainischen Krieg: Eine umfassende Studie

Memes y estrategias narrativas en la guerra ruso-ucraniana: un estudio exhaustivo

Mèmes et stratégies narratives dans la guerre russo-ukrainienne : une étude approfondie

रूस-यूक्रेनी युद्ध में मीम्स और कथात्मक रणनीतियाँ: एक व्यापक अध्ययन

ロシア・ウクライナ戦争におけるミームと物語戦略：包括的研究

러시아-우크라이나 전쟁의 밈과 서사 전략: 종합 연구

Memes e estratégias narrativas na guerra russo-ucraniana: um estudo abrangente

Мемы и нарративные стратегии в российско-украинской войне: комплексное исследование

Rus-Ukrayna Savaşında Memler ve Anlatı Stratejileri: Kapsamlı Bir Çalışma

Memes và chiến lược kể chuyện trong Chiến tranh Nga-Ukraina: Một nghiên cứu toàn diện

俄乌战争中的模因与叙事策略：综合研究

HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today! Tell us about yourself! I’m a Senior Product Designer at PolyAI, focusing on building a conversational AI platform to help callers solve their issues without wasting time waiting for an available agent. Within the last nine years, I made a transition from Graphic design to UI/UX design at a design agency and then to Product design and had the pleasure of working with various great tech-led companies such as SberTech, Gett, and SpatialChat. Before the design field, I studied accounting and developed a passion for data and analytics that help me work closer with a business right now. Why did you choose this field in the first place? I had quite a rough and eventful path with the wrong career decision before I entered this field. At the time, when I should’ve selected the course at the university, I didn’t think that I could select something else apart from accounting because it was pretty straightforward, and I gave up on following my dream to become a designer. A few years later, when I wrote my work for a master’s degree and had a few years of working experience as an accountant, I understood that I didn’t love what I was doing and the only thing I loved to do was design that I learned as a hobby. It took half a year to get my first job as a Graphic designer, and then I dived into UI/UX design out of curiosity. During my first few years as a UI/UX designer at an agency, I was extremely interested in helping businesses reach their goals via interfaces but working in an agency as an outsource designer didn’t allow me to dive into business problems that deep. I made one more tiny transition from UI/UX design into Product design that enabled my full potential. I always had a passion for the design field, and by being a Product designer, I’m not just a person who designs interfaces but also helps users with their needs and assists a business in reaching goals with any technical constraints. For the last six years of work as a Product designer, I’m still extremely excited about my work and love this rapidly evolving field. What tech are you most excited/passionate about right now and why? I'm totally excited about AI and machine learning. The way these technologies are transforming everyday life and bringing simpler and smarter solutions to companies is fascinating. As a part of the AI startup, I witnessed how this technology saves companies money and helps callers faster than a human. Also, I use a lot of AI tools that already improved my working process and helped me to save time for more crucial parts of my work. This is an exciting time to live in, and I can’t wait to see what else this field can bring us in the future. What tech are you most worried about right now and why? The same as excitement, I’m most worried about AI and the ethical implications, as well as potential fraud via this powerful technology. This technology is very advanced and accessible without severe legal restrictions and regulations right now. There have already been a few cases of fraud that involved AI technology. Since there aren't many tools that can detect and prevent that kind of misuse of AI, this field could bring risks to individual privacy and data security. What are your hobbies and interests outside of tech? I’m an absolute video game fan. I love how video games create the ambiance via sounds, stories, visuals, and actions. Besides exploring new worlds, checking my reflexes, and just having fun, I use this activity as a source of inspiration for my work on interfaces. I’m in love with this field and have the same passion as I am with product design. I even tried CG art, so I have massive respect and admiration for everyone involved in game development. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? The biggest challenges I've faced as a woman in tech are fighting my impostor syndrome and biases, which led me to a lower salary rate, lack of confidence, and stagnation in my career growth. All of that caused situations where I've often found myself silent or ignored by colleagues when other people confidently shared potentially risky ideas. Regarding the impostor syndrome, it pushed me to spend many hours learning the design basis, psychology, and everything else that is directly connected to my work to prove that I’m not an impostor and have enough knowledge. It boosted my career and confidence a lot when other designers started asking for advice, a few lessons, or mentorship.All this hard work and recognition from other designers made me confident, so I started taking the initiative in conversations, pitching my ideas, and exceeding expectations when someone underestimated my skills for certain reasons. Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? Unfortunately, I faced many stories worth sharing, but I’ll skip the most common ones when the ideas I pitched were ignored, and when a male colleague shared the same idea a few moments later, it was a “breakthrough idea.” One situation happened in one of the latest companies I worked for; a Product manager I worked with constantly allowed abusive and respectless comments about the work of female colleagues with attempts to cut off reasonable ideas. As you can guess, the dialogues with male colleagues were full of respect and trust in their professionalism without any doubts in pitched ideas. The climax of this situation for me was when this Product manager, in a very rude form with an accent in my gender, declined my design solution without any arguments on a group call. After that call, I initiated a dialog with this product manager where I questioned the situation, and the only answer I received was a joking response that I had misunderstood. Unfortunately, the actions I made didn't help the situation, nor did chats with my direct lead and HR department. This situation taught me to stop silently ignoring such respectless behavior and ask a person a reason for that behavior or elaborate on the decision even when they commented on someone else. Eventually, it helped, and other colleagues started noticing the change, but it took a few months. This story is not an attempt to show the negatives but to highlight the way out of it. What was the biggest setback/failure that you faced, and how did you manage it? My biggest setback was spending years studying accounting and even getting a master’s degree. This decision delayed my career and made me work twice as hard to become a designer. Also, this episode of my life shows me what I’m really capable of and that I definitely should pursue my passion. What's your biggest achievement that you're really proud of? My biggest achievement is the skills I've developed during this fight with my impostor syndrome and pivot in my career. All the doubts in myself I had in the past transformed into solid knowledge and experience that I’m more than happy to share with other designers or people who would like to change their careers. As a result of all this, I have received recognition and respect from other designers I was lucky to work with, and I was given the opportunity to guide my design vision in multiple projects. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? In my opinion, the gender gap in the tech industry is the consequence of many stereotypes and expectations regarding social roles for women that were set a long time ago. All these stereotypes and expectations follow women in every sphere, especially at work in the tech industry.Unfortunately, I witnessed situations when highly intelligent women with an interest in tech gave up or didn’t try to enter the industry just because they couldn’t find enough support but heard a lot of stereotypes that tech is not for women. It is especially frustrating when there are not that many women who share their stories to inspire others. To reduce this gender gap, we should continue fighting the stereotypes and misogyny and encourage women to pursue their interests in the tech industry. Also, I strongly believe that having various communities such as “Women in Tech” that provide support, share success stories, and show role models in these fields is extremely important at any stage of a career. Apart from these steps, there must be work done in the educational field to encourage young girls to explore other options because there is still a perception that technology, programming, engineering, and physics are for men. Who is your tech idol? Why? I couldn’t say that I have an idol in the tech industry, but I have a person who has inspired me a lot on my career path. I’m talking about amazingly intelligent and skilled Sasha Ermolenko, who is currently the Director of the Product Design Department at VK, which is one the biggest social networks in Russia. Each time, she offers unique insights about the design sphere, shares her strong leadership skills and expresses her thoughts about the gender gap in the design sphere. Do you have any advice for aspiring girls who want to join the field?** Yes, your career path could be tough for you in this field, but don’t give up and be afraid to follow your dreams. All your current skills, unique perspectives, interests, and strengths are already your greatest assets in this dynamic industry.Text me if you need support! HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today ! this interview series with women in tech Share your story today Tell us about yourself! I’m a Senior Product Designer at PolyAI, focusing on building a conversational AI platform to help callers solve their issues without wasting time waiting for an available agent. Within the last nine years, I made a transition from Graphic design to UI/UX design at a design agency and then to Product design and had the pleasure of working with various great tech-led companies such as SberTech, Gett, and SpatialChat. Before the design field, I studied accounting and developed a passion for data and analytics that help me work closer with a business right now. Why did you choose this field in the first place? Why did you choose this field in the first place? I had quite a rough and eventful path with the wrong career decision before I entered this field. At the time, when I should’ve selected the course at the university, I didn’t think that I could select something else apart from accounting because it was pretty straightforward, and I gave up on following my dream to become a designer. A few years later, when I wrote my work for a master’s degree and had a few years of working experience as an accountant, I understood that I didn’t love what I was doing and the only thing I loved to do was design that I learned as a hobby. It took half a year to get my first job as a Graphic designer, and then I dived into UI/UX design out of curiosity. During my first few years as a UI/UX designer at an agency, I was extremely interested in helping businesses reach their goals via interfaces but working in an agency as an outsource designer didn’t allow me to dive into business problems that deep. I made one more tiny transition from UI/UX design into Product design that enabled my full potential. I always had a passion for the design field, and by being a Product designer, I’m not just a person who designs interfaces but also helps users with their needs and assists a business in reaching goals with any technical constraints. For the last six years of work as a Product designer, I’m still extremely excited about my work and love this rapidly evolving field. What tech are you most excited/passionate about right now and why? I'm totally excited about AI and machine learning. The way these technologies are transforming everyday life and bringing simpler and smarter solutions to companies is fascinating. As a part of the AI startup, I witnessed how this technology saves companies money and helps callers faster than a human. Also, I use a lot of AI tools that already improved my working process and helped me to save time for more crucial parts of my work. This is an exciting time to live in, and I can’t wait to see what else this field can bring us in the future. What tech are you most worried about right now and why? What tech are you most worried about right now and why? The same as excitement, I’m most worried about AI and the ethical implications, as well as potential fraud via this powerful technology. This technology is very advanced and accessible without severe legal restrictions and regulations right now. There have already been a few cases of fraud that involved AI technology. Since there aren't many tools that can detect and prevent that kind of misuse of AI, this field could bring risks to individual privacy and data security. What are your hobbies and interests outside of tech? I’m an absolute video game fan. I love how video games create the ambiance via sounds, stories, visuals, and actions. Besides exploring new worlds, checking my reflexes, and just having fun, I use this activity as a source of inspiration for my work on interfaces. I’m in love with this field and have the same passion as I am with product design. I even tried CG art, so I have massive respect and admiration for everyone involved in game development. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? The biggest challenges I've faced as a woman in tech are fighting my impostor syndrome and biases, which led me to a lower salary rate, lack of confidence, and stagnation in my career growth. All of that caused situations where I've often found myself silent or ignored by colleagues when other people confidently shared potentially risky ideas. Regarding the impostor syndrome, it pushed me to spend many hours learning the design basis, psychology, and everything else that is directly connected to my work to prove that I’m not an impostor and have enough knowledge. It boosted my career and confidence a lot when other designers started asking for advice, a few lessons, or mentorship. All this hard work and recognition from other designers made me confident, so I started taking the initiative in conversations, pitching my ideas, and exceeding expectations when someone underestimated my skills for certain reasons. Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? Unfortunately, I faced many stories worth sharing, but I’ll skip the most common ones when the ideas I pitched were ignored, and when a male colleague shared the same idea a few moments later, it was a “breakthrough idea.” One situation happened in one of the latest companies I worked for; a Product manager I worked with constantly allowed abusive and respectless comments about the work of female colleagues with attempts to cut off reasonable ideas. As you can guess, the dialogues with male colleagues were full of respect and trust in their professionalism without any doubts in pitched ideas. The climax of this situation for me was when this Product manager, in a very rude form with an accent in my gender, declined my design solution without any arguments on a group call. After that call, I initiated a dialog with this product manager where I questioned the situation, and the only answer I received was a joking response that I had misunderstood. Unfortunately, the actions I made didn't help the situation, nor did chats with my direct lead and HR department. This situation taught me to stop silently ignoring such respectless behavior and ask a person a reason for that behavior or elaborate on the decision even when they commented on someone else. Eventually, it helped, and other colleagues started noticing the change, but it took a few months. This story is not an attempt to show the negatives but to highlight the way out of it. What was the biggest setback/failure that you faced, and how did you manage it? What was the biggest setback/failure that you faced, and how did you manage it? My biggest setback was spending years studying accounting and even getting a master’s degree. This decision delayed my career and made me work twice as hard to become a designer. Also, this episode of my life shows me what I’m really capable of and that I definitely should pursue my passion. What's your biggest achievement that you're really proud of? What's your biggest achievement that you're really proud of? My biggest achievement is the skills I've developed during this fight with my impostor syndrome and pivot in my career. All the doubts in myself I had in the past transformed into solid knowledge and experience that I’m more than happy to share with other designers or people who would like to change their careers. As a result of all this, I have received recognition and respect from other designers I was lucky to work with, and I was given the opportunity to guide my design vision in multiple projects. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? In my opinion, the gender gap in the tech industry is the consequence of many stereotypes and expectations regarding social roles for women that were set a long time ago. All these stereotypes and expectations follow women in every sphere, especially at work in the tech industry. Unfortunately, I witnessed situations when highly intelligent women with an interest in tech gave up or didn’t try to enter the industry just because they couldn’t find enough support but heard a lot of stereotypes that tech is not for women. It is especially frustrating when there are not that many women who share their stories to inspire others. To reduce this gender gap, we should continue fighting the stereotypes and misogyny and encourage women to pursue their interests in the tech industry. Also, I strongly believe that having various communities such as “Women in Tech” that provide support, share success stories, and show role models in these fields is extremely important at any stage of a career. Apart from these steps, there must be work done in the educational field to encourage young girls to explore other options because there is still a perception that technology, programming, engineering, and physics are for men. Who is your tech idol? Why? I couldn’t say that I have an idol in the tech industry, but I have a person who has inspired me a lot on my career path. I’m talking about amazingly intelligent and skilled Sasha Ermolenko, who is currently the Director of the Product Design Department at VK, which is one the biggest social networks in Russia. Each time, she offers unique insights about the design sphere, shares her strong leadership skills and expresses her thoughts about the gender gap in the design sphere. Do you have any advice for aspiring girls who want to join the field?** Yes, your career path could be tough for you in this field, but don’t give up and be afraid to follow your dreams. All your current skills, unique perspectives, interests, and strengths are already your greatest assets in this dynamic industry. Text me if you need support!

Elena Shabanova, Senior Product Designer at PolyAI: Women in Tech Interview

Stories, News & Mentions about VK

Vk’s Stories on HackerNoon

Vk's News & Mentions